This event has been postponed until further notice.

Title: Robust and Real-time Detection of Patient Deterioration from Multimodal Intensive Monitoring Data

Date: Tuesday, January 12, 2021

Time: 1:30 pm – 3:00 pm (EST)

Location: https://bluejeans.com/5058397778

Yanbo Xu

Machine Learning PhD Student

School of Computational Science and Engineering

College of Computing
Georgia Institute of Technology

Committee

Dr. Chao Zhang (Advisor) – School of Computational Science and Engineering, Georgia Institute of Technology

Dr. Yao Xie – H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Yajun Mei – H. Milton Stewart School of Industrial and System Engineering, Georgia Institute of Technology

Abstract

With the wide adoption of Electronic Health Record (EHR) systems across the states in the past decade, vast amount of patient data become available for machine learning researchers to develop accurate models that can potentially improve patient managements and outcomes. Developing robust and real-time computational tools for assisting critical care in the intensive care units (ICUs) is particularly of great value because of the high cost (taking about 15-20% of hospital budgets) of the care, high mortality rate (ranging from 8-19% or about 500,000 deaths annually) in the units, and the extremely large detailed data that have been collected but mostly wasted. That is, an overwhelming amount of devices such as vital sign monitors, mechanical ventilators, dialysis machines, etc, are often used in the ICUs to monitor all aspects of patients, but even the most experienced and knowledgeable intensivists hardly can understand such massive information on a continuous basis. Thus we have completed works on developing dynamic machine learning models and recurrent neural networks that can accurately detect patient deterioration and serve feedbacks in real time from integrating the large amount of multimodal continuous monitoring data. 

In this proposal, we focus on developing robust detection methods that customize reliable feedbacks based on individual patients. We propose two ways to achieve the goal: model calibration and personalized thresholding. With the proposed scalable calibration method, our model can generate reliable confidence scores that can be later used for raising alarms only if the prediction confidence is high. Combining with the proposed personalized thresholding method, these generated alarms can be customized and take account of the trade-off between early detection and low false alarm rates.